{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook regroups the code sample of the video below, which is a part of the [Hugging Face course](https://huggingface.co/course)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cellView": "form"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/W_gMJF0xomE?rel=0&amp;controls=0&amp;showinfo=0\" frameborder=\"0\" allowfullscreen></iframe>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#@title\n",
    "from IPython.display import HTML\n",
    "\n",
    "HTML('<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/W_gMJF0xomE?rel=0&amp;controls=0&amp;showinfo=0\" frameborder=\"0\" allowfullscreen></iframe>')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Install the Transformers and Datasets libraries to run this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip install datasets transformers[sentencepiece]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Reusing dataset glue (/home/sgugger/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
       "        num_rows: 3668\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
       "        num_rows: 408\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
       "        num_rows: 1725\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "raw_datasets = load_dataset(\"glue\", \"mrpc\")\n",
    "raw_datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['sentence1', 'sentence2', 'label', 'idx'],\n",
       "    num_rows: 3668\n",
       "})"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_datasets[\"train\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'idx': 6,\n",
       " 'label': 0,\n",
       " 'sentence1': 'The Nasdaq had a weekly gain of 17.27 , or 1.2 percent , closing at 1,520.15 on Friday .',\n",
       " 'sentence2': 'The tech-laced Nasdaq Composite .IXIC rallied 30.46 points , or 2.04 percent , to 1,520.15 .'}"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_datasets[\"train\"][6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'idx': [0, 1, 2, 3, 4],\n",
       " 'label': [1, 0, 1, 0, 1],\n",
       " 'sentence1': ['Amrozi accused his brother , whom he called \" the witness \" , of deliberately distorting his evidence .',\n",
       "  \"Yucaipa owned Dominick 's before selling the chain to Safeway in 1998 for $ 2.5 billion .\",\n",
       "  'They had published an advertisement on the Internet on June 10 , offering the cargo for sale , he added .',\n",
       "  'Around 0335 GMT , Tab shares were up 19 cents , or 4.4 % , at A $ 4.56 , having earlier set a record high of A $ 4.57 .',\n",
       "  'The stock rose $ 2.11 , or about 11 percent , to close Friday at $ 21.51 on the New York Stock Exchange .'],\n",
       " 'sentence2': ['Referring to him as only \" the witness \" , Amrozi accused his brother of deliberately distorting his evidence .',\n",
       "  \"Yucaipa bought Dominick 's in 1995 for $ 693 million and sold it to Safeway for $ 1.8 billion in 1998 .\",\n",
       "  \"On June 10 , the ship 's owners had published an advertisement on the Internet , offering the explosives for sale .\",\n",
       "  'Tab shares jumped 20 cents , or 4.6 % , to set a record closing high at A $ 4.57 .',\n",
       "  'PG & E Corp. shares jumped $ 1.63 or 8 percent to $ 21.03 on the New York Stock Exchange on Friday .']}"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_datasets[\"train\"][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'sentence1': Value(dtype='string', id=None),\n",
       " 'sentence2': Value(dtype='string', id=None),\n",
       " 'label': ClassLabel(num_classes=2, names=['not_equivalent', 'equivalent'], names_file=None, id=None),\n",
       " 'idx': Value(dtype='int32', id=None)}"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_datasets[\"train\"].features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading cached processed dataset at /home/sgugger/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-5c7a60253cea912b.arrow\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a6e5173bdc04414c90ce2df2415ed2ce",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=408.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading cached processed dataset at /home/sgugger/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-b13bcd85aad070e8.arrow\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "{'train': ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'], 'validation': ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids'], 'test': ['attention_mask', 'idx', 'input_ids', 'label', 'sentence1', 'sentence2', 'token_type_ids']}\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoTokenizer\n",
    "\n",
    "checkpoint = \"bert-base-cased\"\n",
    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
    "\n",
    "def tokenize_function(example):\n",
    "    return tokenizer(\n",
    "        example[\"sentence1\"], example[\"sentence2\"], padding=\"max_length\", truncation=True, max_length=128\n",
    "    )\n",
    "\n",
    "tokenized_datasets = raw_datasets.map(tokenize_function)\n",
    "print(tokenized_datasets.column_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading cached processed dataset at /home/sgugger/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-2b2682faffe74c3f.arrow\n",
      "Loading cached processed dataset at /home/sgugger/.cache/huggingface/datasets/glue/mrpc/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-78d79fc323f0156c.arrow\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8bca843d13fd436e8325e528ec1eddb2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=2.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoTokenizer\n",
    "\n",
    "checkpoint = \"bert-base-cased\"\n",
    "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
    "\n",
    "def tokenize_function(examples):\n",
    "    return tokenizer(\n",
    "        examples[\"sentence1\"], examples[\"sentence2\"], padding=\"max_length\", truncation=True, max_length=128\n",
    "    )\n",
    "\n",
    "tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['attention_mask', 'input_ids', 'labels', 'token_type_ids'],\n",
       "    num_rows: 3668\n",
       "})"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenized_datasets = tokenized_datasets.remove_columns([\"idx\", \"sentence1\", \"sentence2\"])\n",
    "tokenized_datasets = tokenized_datasets.rename_column(\"label\", \"labels\")\n",
    "tokenized_datasets = tokenized_datasets.with_format(\"tensorflow\")\n",
    "tokenized_datasets[\"train\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "small_train_dataset = tokenized_datasets[\"train\"].select(range(100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "name": "Hugging Face Datasets overview (TensorFlow)",
   "provenance": []
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
